Literature DB >> 31630113

Using normalisation process theory to understand workflow implications of decision support implementation across diverse primary care settings.

Rebecca G Mishuris1, Joseph Palmisano2, Lauren McCullagh3, Rachel Hess4, David A Feldstein5, Paul D Smith5, Thomas McGinn3, Devin M Mann6.   

Abstract

BACKGROUND: Effective implementation of technologies into clinical workflow is hampered by lack of integration into daily activities. Normalisation process theory (NPT) can be used to describe the kinds of 'work' necessary to implement and embed complex new practices. We determined the suitability of NPT to assess the facilitators, barriers and 'work' of implementation of two clinical decision support (CDS) tools across diverse care settings.
METHODS: We conducted baseline and 6-month follow-up quantitative surveys of clinic leadership at two academic institutions' primary care clinics randomised to the intervention arm of a larger study. The survey was adapted from the NPT toolkit, analysing four implementation domains: sense-making, participation, action, monitoring. Domains were summarised among completed responses (n=60) and examined by role, institution, and time.
RESULTS: The median score for each NPT domain was the same across roles and institutions at baseline, and decreased at 6 months. At 6 months, clinic managers' participation domain (p=0.003), and all domains for medical directors (p<0.003) declined. At 6 months, the action domain decreased among Utah respondents (p=0.03), and all domains decreased among Wisconsin respondents (p≤0.008).
CONCLUSIONS: This study employed NPT to longitudinally assess the implementation barriers of new CDS. The consistency of results across participant roles suggests similarities in the work each role took on during implementation. The decline in engagement over time suggests the need for more frequent contact to maintain momentum. Using NPT to evaluate this implementation provides insight into domains which can be addressed with participants to improve success of new electronic health record technologies. TRIAL REGISTRATION NUMBER: NCT02534987. © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Entities:  

Keywords:  clinical decision support; electronic health records; implementation; normalization process theory; quantitative survey

Mesh:

Year:  2019        PMID: 31630113     DOI: 10.1136/bmjhci-2019-100088

Source DB:  PubMed          Journal:  BMJ Health Care Inform        ISSN: 2632-1009


  1 in total

1.  Application of a Machine Learning-Based Decision Support Tool to Improve an Injury Surveillance System Workflow.

Authors:  Jesani Catchpoole; Gaurav Nanda; Kirsten Vallmuur; Goshad Nand; Mark Lehto
Journal:  Appl Clin Inform       Date:  2022-05-29       Impact factor: 2.762

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.